Grover's Algorithm and Many-Valued Quantum Logic
- URL: http://arxiv.org/abs/2001.06316v1
- Date: Fri, 17 Jan 2020 14:02:50 GMT
- Title: Grover's Algorithm and Many-Valued Quantum Logic
- Authors: Samuel Hunt and Maximilien Gadouleau
- Abstract summary: We investigate Grover's algorithm under a generalised quantum circuit model.
We analyse the structural and behavioural properties while preserving the semantics.
We conclude by demonstrating that the generalised procedure retains $O(sqrtN)$ time complexity.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As the engineering endeavour to realise quantum computers progresses, we
consider that such machines need not rely on binary as their de facto unit of
information. We investigate Grover's algorithm under a generalised quantum
circuit model, in which the information and transformations can be expressed in
any arity, and analyse the structural and behavioural properties while
preserving the semantics; namely, searching for the unique preimage to an
output a function. We conclude by demonstrating that the generalised procedure
retains $O(\sqrt{N})$ time complexity.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
We show that a quantum processor can correctly solve the basic classification task considered.
With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
arXiv Detail & Related papers (2024-06-17T18:20:51Z) - Deterministic Search on Complete Bipartite Graphs by Continuous Time Quantum Walk [0.8057006406834466]
This paper presents a deterministic search algorithm on complete bipartite graphs.
We address the most general case of multiple marked states, so there is a problem of estimating the number of marked states.
We construct a quantum counting algorithm based on the spectrum structure of the search operator.
arXiv Detail & Related papers (2024-04-02T05:09:33Z) - Majorization-based benchmark of the complexity of quantum processors [105.54048699217668]
We numerically simulate and characterize the operation of various quantum processors.
We identify and assess quantum complexity by comparing the performance of each device against benchmark lines.
We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
arXiv Detail & Related papers (2023-04-10T23:01:10Z) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - Detailed Account of Complexity for Implementation of Some Gate-Based
Quantum Algorithms [55.41644538483948]
In particular, some steps of the implementation, as state preparation and readout processes, can surpass the complexity aspects of the algorithm itself.
We present the complexity involved in the full implementation of quantum algorithms for solving linear systems of equations and linear system of differential equations.
arXiv Detail & Related papers (2021-06-23T16:33:33Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z) - Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow [7.619626059034881]
We propose an efficient scheme for quantum circuit equivalence checking.
The proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less.
arXiv Detail & Related papers (2020-09-04T19:58:53Z) - Quantum Search for Scaled Hash Function Preimages [1.3299507495084417]
We present the implementation of Grover's algorithm in a quantum simulator to perform a quantum search for preimages of two scaled hash functions.
We show that strategies that suggest a shortcut based on sampling the quantum register after a few steps of Grover's algorithm can only provide some marginal practical advantage in terms of error mitigation.
arXiv Detail & Related papers (2020-09-01T18:00:02Z) - Compiling single-qubit braiding gate for Fibonacci anyons topological
quantum computation [0.0]
Topological quantum computation is an implementation of a quantum computer in a way that radically reduces decoherence.
Topological qubits are encoded in the topological evolution of two-dimensional quasi-particles called anyons.
arXiv Detail & Related papers (2020-08-08T15:34:03Z) - Topological Quantum Compiling with Reinforcement Learning [7.741584909637626]
We introduce an efficient algorithm that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set.
Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
arXiv Detail & Related papers (2020-04-09T18:00:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.